Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add chrisallenlane/claude-swe-workflows --skill review-archgit clone --depth 1 https://github.com/chrisallenlane/claude-swe-workflowsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/chrisallenlane/claude-swe-workflows/review-arch)<a href="https://agentmods.dev/skills/chrisallenlane/claude-swe-workflows/review-arch"><img src="https://agentmods.dev/badge/skills/chrisallenlane/claude-swe-workflows/review-arch/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/chrisallenlane/claude-swe-workflows/review-arch"><img src="https://agentmods.dev/badge/skills/chrisallenlane/claude-swe-workflows/review-arch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00060 | $0.03194 |
| Opus 5 | $0.00030 | $0.01597 |
| Sonnet 5 | $0.00012 | $0.00639 |
| Haiku 4.5 | $0.00006 | $0.00319 |
Grade A, and why
review-arch scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arch Review — Advisory Architectural Analysis
Analyzes codebase architecture and produces a target blueprint via noun analysis. Advisory only. The skill does not implement changes — it surfaces opportunities and offers to cut tickets so the work can be picked up by implementation skills. The offer goes to the operator (human or orchestrator) regardless of caller; orchestrators apply their own autonomy judgment to approve / edit / decline.
Philosophy
Review and implementation are different concerns. A skill that does both makes both worse — implementation pressure compromises the review, and review pressure compromises the implementation. The plugin is moving /review-* skills toward advisory-only over time (see the "Advisory aspiration" section of references/autonomy.md); /review-arch is the first concrete step in that direction.
Clarity through organization is the goal. Every module should have a clear identity — a domain noun it owns. Functions should live where a reader expects to find them. DRY and Prune serve this organizational goal, not the other way around.
Recommend boldly. The analysis agent surfaces every opportunity it finds, even uncertain ones — the operator can always reject a recommendation when reviewing the plan. The skill's job is to see, not to act.
Single workflow for everyone. The skill's workflow is identical whether a human operator or an orchestrator (/lead-refactor, /implement-project, /lead-project) is at the receiving end. After the analysis, the skill presents a proposed ticket structure for the recommended work; the operator (human or orchestrator) approves, edits, or declines. Orchestrators apply their own autonomy judgment per references/autonomy.md — declining items they intend to implement inline, approving items they want tracked for later.
Workflow Overview
┌─────────────────────────────────────────────────────────────────┐
│ ARCH REVIEW WORKFLOW (advisory) │
├─────────────────────────────────────────────────────────────────┤
│ 1. Determine scope │
│ 2. Spawn swe-arch-reviewer agent (full analysis) │
│ → returns dead code list + target blueprint │
│ 3. Present analysis to operator │
│ 4. Iterate on plan with operator │
│ 5. Offer to cut tickets │
│ ├─ Preview ticket set │
│ ├─ Operator approves / edits / declines │
│ └─ Create approved tickets in tracker with labels │
│ 6. Completion summary │
│ └─ Tickets created (or "no tickets — analysis only") │
└─────────────────────────────────────────────────────────────────┘
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 278 lines · 60 tokens per session scan A 24cc4bbea5fa
review-arch is a skill published in the GitHub repository chrisallenlane/claude-swe-workflows (18 stars, last pushed 3mo ago), licensed MIT. It adds 60 tokens to every session and 3,194 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
afc:learner
Review and promote learned patterns to project rules.
afc:pr-comment
Post structured review comments to GitHub PR.
afc:architect
Architecture analysis and design review.
afc:resolve
Address LLM bot review comments on PR — fix valid issues, dismiss false positives.
afc:review
Code review — review code, analyze PR diff, evaluate quality and correctness.
review-loop
Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified"…